🐍 Python Setup Test

A comprehensive testing notebook to verify Python, Conda, and package installations are working correctly.

🎯 Purpose

This repository contains a Jupyter notebook that tests:

• Python installation and version
• Conda environment management
• Core scientific computing libraries
• Machine learning libraries
• Data visualization capabilities
• A fun lightweight LLM demo!

🚀 Quick Start

Prerequisites

• Conda - Download and install from the official website
  • Windows (Command Prompt): Miniconda for Windows
  • Windows (PowerShell): Miniconda for Windows PowerShell
  • macOS: Miniconda for macOS
  • Linux: Miniconda for Linux
• Git (for cloning)

Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd python-setup-test

2. Create and activate the conda environment:

   # Create the environment from the yml file
   conda env create -f environment.yml
   
   # Activate the environment
   conda activate python-setup-test

3. Start Jupyter Notebook:

   jupyter notebook

4. Open and run the notebook:

   • Open Python_Setup_Test.ipynb
   • Run all cells (Cell → Run All)

📦 What's Included

Environment (environment.yml)

• Python 3.11
• Core libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
• ML libraries: PyTorch, Transformers, Accelerate
• Jupyter and notebook support

Test Notebook (Python_Setup_Test.ipynb)

• Test 1: Python version and environment check
• Test 2: Core scientific computing libraries
• Test 3: Machine learning libraries
• Test 4: Fun data visualization demo
• Test 5: Machine learning classification demo
• Test 6: Lightweight LLM interaction demo

🎨 Features

Data Visualization

• Beautiful sine wave plots
• Scatter plots with color mapping
• Histograms and distributions
• Heatmaps using Seaborn

Machine Learning

• Synthetic dataset generation
• Random Forest classification
• Performance metrics
• Feature importance analysis

AI/LLM Demo

• GPT-2 Medium model (~500MB)
• High-quality text generation
• Creative writing and storytelling
• Fast and efficient - perfect balance of capability and speed

🛠️ Troubleshooting

Common Issues

1. Environment not activating:

   conda activate python-setup-test

2. Package import errors:

   conda list  # Check installed packages
   conda env create -f environment.yml --force  # Recreate environment

3. LLM demo fails:

   • Check internet connection (model downloads ~500MB on first run)
   • The core tests will still work if LLM demo fails
   • Falls back to GPT-2 Small if Medium fails

4. Jupyter not starting:

   conda install jupyter
   jupyter notebook

Manual Package Installation

If the environment creation fails, install packages manually:

conda activate python-setup-test
conda install python=3.11 numpy pandas matplotlib seaborn scikit-learn jupyter
pip install torch transformers accelerate

📝 Customization

Adding More Tests

Edit Python_Setup_Test.ipynb to add your own tests:

• Add new library imports
• Create custom visualizations
• Test specific functionality

Modifying Environment

Edit environment.yml to:

• Change Python version
• Add/remove packages
• Modify package versions

🤝 Contributing

Feel free to:

• Add more test cases
• Improve the visualizations
• Add more ML demos
• Enhance the LLM interaction

📄 License

This project is open source and available under the MIT License.

🎉 Success!

If all tests pass, your Python setup is working perfectly! You're ready to start your data science and machine learning journey.

Happy coding! 🐍✨